Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Semantically en enhanced information retrieval: an ontology-based aprroach

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, enero de 2009Bibliogr.: [227]-240 p

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Knowledge and Management Models for Sustainable Growth

In the last years sustainability has become a topic of global concern and a key issue in the strategic agenda of both business organizations and public authorities and organisations. Significant changes in business landscape, the emergence of new technology, including social media, the pressure of new social concerns, have called into question established conceptualizations of competitiveness, wealth creation and growth. New and unaddressed set of issues regarding how private and public organisations manage and invest their resources to create sustainable value have brought to light. In particular the increasing focus on environmental and social themes has suggested new dimensions to be taken into account in the value creation dynamics, both at organisations and communities level. For companies the need of integrating corporate social and environmental responsibility issues into strategy and daily business operations, pose profound challenges, which, in turn, involve numerous processes and complex decisions influenced by many stakeholders. Facing these challenges calls for the creation, use and exploitation of new knowledge as well as the development of proper management models, approaches and tools aimed to contribute to the development and realization of environmentally and socially sustainable business strategies and practices

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Towards a framework of deep diversity: Identity and invisibility in the Indian diaspora in New Zealand

Using the Indian diaspora in New Zealand as a case study, this thesis examines how state categorisation practices and nation building narratives have constructed and racialised migrant minorities, such as Indians, in particular ways. It does so through a review of the historical settlement narrative and census records that have tended to erase early ethnic minority presence from what is seen as a predominantly bicultural encounter. Aotearoan colonial society has tended to render early Indian presence in New Zealand invisible. This pattern remains perceptible in the prolonged use of homogenising ethnic categories utilised throughout the history of the New Zealand census that obfuscate the extent of ethnic minority diversification with specific reference to the Indian community. The thesis critiques state constructions of ethnic identity through (1) the presentation of alternative historical narratives that more appropriately demonstrate the presence of non-Māori non-European minorities at first contact; (2) an examination of minority reporting in the New Zealand census during the period of early European settlement; and (3) an analysis of data from a survey of the Indian community in New Zealand. The survey data on self-reported experiences of discrimination underscore the importance of ethnic self-identification and the use of more heterogeneous categories for appropriate minority recognition. At a theoretical level, the thesis outlines a novel framework for diversity governance, known as deep diversity, which is informed by an interdisciplinary methodological and theoretical approach that draws on the disciplines of anthropology, demography, history, and policy studies. This framework rethinks current policy approaches that position minorities as beneficiaries of policies designed for their social uplift and integration into majority society, and instead places the onus of social integration on both minorities and majorities. The framework is applied to an analysis of qualitative data from historical sources that fundamentally question New Zealand’s existing bicultural settlement narrative; to quantitative data from both historical and contemporary census records; and to a self-administered predominantly web-based survey of 1,124 Indian respondents using a snowball sampling method. This thesis presents an alternative historical settlement narrative that positions Indians as participating, along with Europeans, in first contact encounters with Māori in Aotearoa. Past and present census analysis also reveals the extent of historic Indian invisibility, and demonstrates continued state use of enumeration techniques that obscure and homogenise the diversity that exists within the Indian population. The survey results focus on the themes of identity and discrimination, the analysis of which offers insights about the importance of ethnic self-identification, the continued presence of discrimination, and the use of more heterogeneous categories for appropriate minority recognition. Specific survey results show that respondents, while identifying as ‘Indian’ on the census, favour terms that cite hyphenated nationality or ethnicity (e.g. Kiwi-Indian, Indo-Fijian) or regional, religious, linguistic and country of birth identifiers, as significant forms of self-identification. Results on discrimination demonstrate that 48.4% of survey respondents reported being the target of a discrimination event in New Zealand (86.9% of whom are migrants, while 13.1% were born in New Zealand). When queried about the presence of discrimination, 90.7% of respondents believe that racism and discrimination currently exist within New Zealand society, while only 9.3% believe that it does not exist. Minority invisibility contributes to social discrimination, and helps perpetuate the shallow diversity management practices in use today. More attention to the importance of appropriate minority self-identification and accommodation, involving majorities in minority integration programmes, and institutional support for a shared national identity, could all help facilitate and promote vital social cohesion strategies in New Zealand. The deep diversity framework articulated in this thesis offers an alternative vision for diversity governance and social cohesion that is appropriate for western liberal democracies with highly pluralised societies such as New Zealand

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area